DIVVY BIKE FULL

In my opinion, a more useful metric is the user’s age. For example, Birth Year is one of the variables provided for a user.

I also grouped the zip codes into regions within the ChicagoLand Area (Downtown, Southside, Northside, Near Northside, Far Northside, and Western Suburbs).įurther data pre-processing revealed even more useful metrics. To fix this, I found a Python library that accessed zip codes using longitude and latitude (which were given correctly). For example, I noticed that the zip codes were incorrect in the Dept of Transportation’s data.

DIVVY BIKE FULL

However, data pre-processing would unlock the full value of the data and correct some errors that are present in the raw download. Let’s take a deeper look to see what we can find out using OmniSci! Data Cleaning and PyMapDĪ quick glance at the data reveals a great deal of useful information like User Type, Starting and Stopping Station Locations and BikeID. Key statistics are provided about each user’s ride, including trip duration, geolocation, start and stop times and much more. To ease congestion, the city of Chicago’s Department of Transportation created a bike-sharing service called Divvy. In fact, a great amount of this data is publicly available for you to donload and analyze.Īnother quality that most major cities have in common is traffic-rush hours are filled with walkers, bikers, taxis and buses. Even the places where you least expect it are passively collecting massive amounts of data: your local coffee shops, street lights and even post offices. You can’t walk one block without feeling the presence of major tech companies. Big data to be exact! Pick any urban center. A modern city lives, breathes and eats data.

Divvy bike